Better May Not Be Fairer: Can Data Augmentation Mitigate Subgroup Degradation?

It is no secret that deep learning models exhibit undesirable behaviors such as learning spurious correlations instead of learning correct relationships between input/output pairs. Prior works on robustness study datasets that mix low-level features to quantify how spurious correlations affect predictions instead of considering natural semantic factors due to limitations in accessing realistic datasets for comprehensive evaluation. To bridge this gap, in this paper we first investigate how natural background colors play a role as spurious features in image classification tasks by manually splitting the test sets of CIFAR10 and CIFAR100 into subgroups based on the background color of each image. We name our datasets CIFAR10-B and CIFAR100-B. We find that while standard CNNs achieve human-level accuracy, the subgroup performances are not consistent, and the phenomenon remains even after data augmentation (DA). To alleviate this issue, we propose FlowAug, a semantic DA method that leverages the decoupled semantic representations captured by a pre-trained generative flow. Experimental results show that FlowAug achieves more consistent results across subgroups than other types of DA methods on CIFAR10 and CIFAR100. Additionally, it shows better generalization performance. Furthermore, we propose a generic metric for studying model robustness to spurious correlations, where we take a macro average on the weighted standard deviations across different classes. Per our metric, FlowAug demonstrates less reliance on spurious correlations. Although this metric is proposed to study our curated datasets, it applies to all datasets that have subgroups or subclasses. Lastly, aside from less dependence on spurious correlations and better generalization on in-distribution test sets, we also show superior out-of-distribution results on CIFAR10.1 and competitive performances on CIFAR10-C and CIFAR100-C.